Researchers have proposed a variety of systems to generate questions based on a given document and a known answer to the question, with the goal of improving QA systems with augmented data [1]} and improving automated writing systems with question prompts for writers [2]}, [3]}.
Some approaches have extended existing sequential generation language models, such as the LSTM and the transformer, to accommodate both the textual context (e.g. a document) and the likely answer to the question, in order to maximize the probability of generating an informative question [4]}, [5]}.
Reinforcement learning can also be leveraged to reward a system for generating questions that are more likely to have interesting answers [6]} and more likely to be relevant to the context [7]}.
Furthermore, work such as [8]} ([8]}) has proposed controllable generation techniques (e.g. increased difficulty) to encourage less generic questions.
The foundation of this line of work has been to collect human-generated questions from a variety of domains, including Wikipedia [10]}, Stack Overflow [11]}, and Twitter [12]}.
This study builds on the lines of controlled generation and online data collection by testing several variants of reader-aware question generation on data from online forums, which provide questions and documents across many domains.
